{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 10. 活性分子分类\n",
    "\n",
    "- 机器学习的**分类模型**是指一种能够根据输入特征将样本划分到不同类别的算法模型。模型通过学习已有数据（训练集）中的特征与类别之间的关系，进而对未知数据进行分类预测。常见分类模型有决策树、支持向量机（SVM）、随机森林、神经网络等。\n",
    "- 机器学习分类模型的定义\n",
    "    - 任务类型：将数据样本分配到预定义的类别中，比如“活性”或“非活性”。\n",
    "    - 输入：一组特征（如分子结构描述符、指纹、物理化学性质）。\n",
    "    - 输出：类别标签（如“有活性化合物” vs “无活性化合物”）。\n",
    "\n",
    "- 在活性化合物研究中的应用\n",
    "    - 活性预测：利用已知的活性和非活性化合物数据，训练模型预测新的化合物是否具有特定靶点的活性，从而筛选潜在药物候选分子。\n",
    "    - 毒性和副作用分类：预测化合物是否存在毒性或潜在的不良反应，辅助早期药物安全性评估。\n",
    "    - 化学空间分类：对化合物进行类别划分，比如分子家族、功能类别，为药物设计提供化学多样性指导。\n",
    "    - 定向化合物设计：基于分类结果调整分子结构设计，提高新分子活性或选择性。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 支持向量机（SVM）\n",
    "\n",
    "- **基本思想**：求解能够正确划分数据集并且几何间隔最大的分离超平面，使得任一类型的数据划分最优。\n",
    "- 对于线性可分的数据集，分离超平面有无穷多个，但是**几何间隔最大**的分离超平面是唯一的。\n",
    "- **几何间隔最大的超平面**：意味着以充分大的确信度对训练数据集进行分类。\n",
    "\n",
    "\n",
    "\n",
    "<img src=\"../img/Svm_separating_hyperplanes_(SVG).svg\" alt=\"支持向量机超平面\" width=\"640px\">  \n",
    "<img src=\"../img/Kernel_Machine.png\" alt=\"支持向量机超平面\" width=\"640px\"> \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 获取数据集\n",
    "\n",
    "- ChEMBL 是一个公开的生物活性化合物数据库，专注于药物发现相关的信息。它由欧洲生物信息学研究所（EBI）维护，收录了大量已知的小分子化合物及其生物活性数据，包括对各种靶点（如蛋白质、酶、受体等）的结合活性、抑制作用、作用机制等信息。\n",
    "\n",
    "- 简单来说，ChEMBL 是药物研发和化学生物学领域非常重要的资源，主要特点包括：\n",
    "\n",
    "    - 化学结构信息：包含化合物的分子结构和相关属性。\n",
    "    - 生物活性数据：包括IC50、Ki、EC50等定量活性数据，显示化合物与生物靶点的作用效果。\n",
    "    - 靶点信息：详细介绍靶点的生物学背景，基因信息等。\n",
    "    - 实验条件和文献引用：连结活性数据的实验背景和支持的科学文献。\n",
    "    - 研究人员常用ChEMBL做药物靶点筛选、活性预测、结构-活性关系（SAR）分析、机器学习模型训练等。\n",
    "\n",
    "\n",
    "##### 案例：tubulin活性化合物的分类模型\n",
    "- Tubulin 是细胞骨架的重要蛋白，主要功能是组成微管（microtubules）。微管是细胞内细胞骨架的关键组成部分，参与细胞形状维护、物质运输、细胞分裂等过程。它是多种抗癌药物（如紫杉醇）的靶点，通过干扰微管动力学抑制癌细胞分裂。\n",
    "- Chembl中检索tubulin，CHEMBL2094134，Activity Charts，点击饼状图，获得分子表单，共计2207条记录\n",
    "- 下载TSV或CSV文件"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 加载与清洗数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这是tubulin活性筛选的分子表达，Standard Value记录了活性测试的浓度，pChEMBL是IC50的负对数。请读取位pandas数据表，然后去除Standard Value位空的行，pChEMBL空值补0.然后以pChEMBL=7为界限分为两类：大于7的有活性（1），小于等于7的为没有活性（0），然后构建SVM分类模型（不优化参数），并计算准确度，精确率，召回率，并绘制混淆矩阵。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split #用于分割数据集\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import ( # 模型性能评估函数\n",
    "    accuracy_score, \n",
    "    precision_score, \n",
    "    recall_score, \n",
    "    confusion_matrix, \n",
    "    classification_report\n",
    ")\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2000, 49)\n"
     ]
    }
   ],
   "source": [
    "# 读取数据\n",
    "df = pd.read_csv('data/tubulin_activities_chembl.tsv', sep='\\t')\n",
    "\n",
    "'''\n",
    "注意处理逻辑，如果Standard Value为空，无法确定该物质的活性强度，\n",
    "直接标注有无活性，有武断的风险\n",
    "'''\n",
    "# 删除Standard Value为空的行\n",
    "df = df.dropna(subset=['Standard Value'])\n",
    "\n",
    "# pChEMBL空值补0\n",
    "# pChEMBL, px: EC50, IC50等的负对数值\n",
    "df['pChEMBL Value'] = df['pChEMBL Value'].fillna(0)\n",
    "\n",
    "# 活性分类：pChEMBL > 7为有活性(1)，否则为无活性(0)\n",
    "df['activity_class'] = (df['pChEMBL Value'] > 7).astype(int) #简洁优雅\n",
    "\n",
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    2000.000000\n",
       "mean        2.157910\n",
       "std         2.702507\n",
       "min         0.000000\n",
       "25%         0.000000\n",
       "50%         0.000000\n",
       "75%         5.322500\n",
       "max         7.700000\n",
       "Name: pChEMBL Value, dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## 看数值分布\n",
    "df['pChEMBL Value'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pChEMBL Value 描述性统计:\n",
      "count    2000.000000\n",
      "mean        2.157910\n",
      "std         2.702507\n",
      "min         0.000000\n",
      "25%         0.000000\n",
      "50%         0.000000\n",
      "75%         5.322500\n",
      "max         7.700000\n",
      "Name: pChEMBL Value, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# 创建图形  \n",
    "plt.figure(figsize=(10, 6))  \n",
    "\n",
    "# 使用seaborn绘制直方图和kde曲线  \n",
    "sns.histplot(data=df, x='pChEMBL Value', stat='density', kde=True, alpha=0.6)  \n",
    "# sns.kdeplot(data=df, x='pChEMBL Value', color='red', linewidth=2)  \n",
    "\n",
    "# 设置标题和标签  \n",
    "plt.title('pChEMBL Value Distribution', fontsize=15)  \n",
    "plt.xlabel('pChEMBL Value', fontsize=12)  \n",
    "plt.ylabel('Density', fontsize=12)  \n",
    "\n",
    "# 显示网格线  \n",
    "plt.grid(axis='y', linestyle='--', alpha=0.7)  \n",
    "\n",
    "# 调整布局  \n",
    "plt.tight_layout()  \n",
    "\n",
    "# 显示图形  \n",
    "plt.show()  \n",
    "\n",
    "# 打印一些描述性统计  \n",
    "print(\"pChEMBL Value 描述性统计:\")  \n",
    "print(df['pChEMBL Value'].describe())  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**如何设定有无活性的分割界线？**\n",
    "- 有很多现实的考量\n",
    "- 对于活性化合物的筛选而言，期望找到活性更高的分子，因此不能将二者均分\n",
    "    - 为了让模型更好的抓取高活性的分子特征，活性分割界限要比较高\n",
    "    - 但是，有活性的分子数会比较少，正负数据集不平衡，对模型训练不利\n",
    "- 以75%分位值，可能是折衷的选择"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    2000.000000\n",
       "mean        2.157910\n",
       "std         2.702507\n",
       "min         0.000000\n",
       "25%         0.000000\n",
       "50%         0.000000\n",
       "75%         5.322500\n",
       "max         7.700000\n",
       "Name: pChEMBL Value, dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = df['pChEMBL Value'].describe()\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "## 分割数据\n",
    "\n",
    "# 活性分类：pChEMBL > 7为有活性(1)，否则为无活性(0)  \n",
    "df['activity_class'] = (df['pChEMBL Value'] > data['75%']).astype(int)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 生成特征矩阵和结果向量（Xy）\n",
    "\n",
    "- 常用：大写的X代表特征矩阵；小写的y代表分类向量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 生成Morgan指纹  \n",
    "from rdkit import Chem\n",
    "from rdkit.Chem import rdFingerprintGenerator\n",
    "fp_generator = rdFingerprintGenerator.GetMorganGenerator(radius=2, fpSize=1024)\n",
    "\n",
    "mols = df['Smiles'].apply(Chem.MolFromSmiles)\n",
    "fps = mols.apply(fp_generator.GetFingerprint)\n",
    "\n",
    "X = np.array([np.array(fp) for fp in fps])  \n",
    "y = df['activity_class'].values  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2000, 1024)\n",
      "(2000,)\n"
     ]
    }
   ],
   "source": [
    "## 注意检查X的形状\n",
    "print(X.shape)\n",
    "print(y.shape)\n",
    "\n",
    "# 这里是二维数组，2000行，1024列； y是2000个元素"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1]), array([1500,  500]))"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(y, return_counts=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 划分数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 划分训练集和测试集  \n",
    "X_train, X_test, y_train, y_test = train_test_split(  \n",
    "    X, y, test_size=0.2, random_state=42  \n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 初次训练尝试"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "模型性能指标：\n",
      "准确度: 0.7300\n",
      "精确率: 0.7083\n",
      "召回率: 0.1441\n",
      "\n",
      "分类报告：\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.73      0.98      0.84       282\n",
      "           1       0.71      0.14      0.24       118\n",
      "\n",
      "    accuracy                           0.73       400\n",
      "   macro avg       0.72      0.56      0.54       400\n",
      "weighted avg       0.72      0.73      0.66       400\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 构建SVM分类器（不优化参数）  \n",
    "svm = SVC(random_state=42)  \n",
    "svm.fit(X_train, y_train)  \n",
    "\n",
    "# 预测  \n",
    "y_pred = svm.predict(X_test)  \n",
    "\n",
    "# 计算评估指标  \n",
    "accuracy = accuracy_score(y_test, y_pred)  \n",
    "precision = precision_score(y_test, y_pred)  \n",
    "recall = recall_score(y_test, y_pred)  \n",
    "\n",
    "print(\"模型性能指标：\")  \n",
    "print(f\"准确度: {accuracy:.4f}\")  \n",
    "print(f\"精确率: {precision:.4f}\")  \n",
    "print(f\"召回率: {recall:.4f}\")  \n",
    "\n",
    "# 打印分类报告  \n",
    "print(\"\\n分类报告：\")  \n",
    "print(classification_report(y_test, y_pred))   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Precision (精确率)：\n",
    "    - 衡量模型预测正类的准确程度\n",
    "    - 计算公式：正确预测的正类样本 / 所有被预测为正类的样本\n",
    "    - 低误报率的指标\n",
    "    - 意味识别活性化合物的误判率低\n",
    "- Recall (召回率)：\n",
    "    - 衡量模型识别正类样本的完整性\n",
    "    - 计算公式：正确预测的正类样本 / 所有实际正类样本\n",
    "    - 低漏报率的指标\n",
    "    - 意味着漏掉活性化合物的概率低\n",
    "- F1-score：\n",
    "    - Precision和Recall的调和平均数\n",
    "    - 综合反映模型在正类样本识别上的性能\n",
    "    - 平衡了精确性和完整性\n",
    "- Support：\n",
    "    - 每个类别的样本数量\n",
    "    - 反映数据集中各类别的分布情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制混淆矩阵  \n",
    "cm = confusion_matrix(y_test, y_pred)  \n",
    "plt.figure(figsize=(8,6))  \n",
    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues',   \n",
    "            xticklabels=['inactive', 'active'],  \n",
    "            yticklabels=['inactive', 'active'])  \n",
    "plt.title('Tubulin, SVM classfication')  \n",
    "plt.xlabel('pred')  \n",
    "plt.ylabel('real')  \n",
    "plt.tight_layout()  \n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 特征数据X经规约/PCA预处理后再训练模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 标准化数据  \n",
    "from sklearn.decomposition import PCA  \n",
    "scaler = StandardScaler()  \n",
    "X_scaled = scaler.fit_transform(X)  \n",
    "\n",
    "# PCA降维  \n",
    "pca = PCA(n_components=0.95)  # 保留95%的方差 \n",
    "    # 在保留95%原始数据信息的前提下，尽可能减少特征维度\n",
    "X_pca = pca.fit_transform(X)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2000, 217)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_pca.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 划分训练集和测试集  \n",
    "X_train, X_test, y_train, y_test = train_test_split(  \n",
    "    X_pca, y, test_size=0.3, random_state=42  \n",
    ")  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "模型性能指标：\n",
      "准确度: 1.0000\n",
      "精确率: 0.0000\n",
      "召回率: 0.0000\n",
      "\n",
      "分类报告：\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00       600\n",
      "\n",
      "    accuracy                           1.00       600\n",
      "   macro avg       1.00      1.00      1.00       600\n",
      "weighted avg       1.00      1.00      1.00       600\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "H:\\WPy64-31280\\python\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "H:\\WPy64-31280\\python\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1565: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 due to no true samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
     ]
    }
   ],
   "source": [
    "svm = SVC(random_state=42)   # 注意保持random_state的值不变，才能和上一次的训练具有可比性\n",
    "svm.fit(X_train, y_train)  \n",
    "\n",
    "# 预测  \n",
    "y_pred = svm.predict(X_test)  \n",
    "\n",
    "# 计算评估指标  \n",
    "accuracy = accuracy_score(y_test, y_pred)  \n",
    "precision = precision_score(y_test, y_pred)  \n",
    "recall = recall_score(y_test, y_pred)  \n",
    "\n",
    "print(\"模型性能指标：\")  \n",
    "print(f\"准确度: {accuracy:.4f}\")  \n",
    "print(f\"精确率: {precision:.4f}\")  \n",
    "print(f\"召回率: {recall:.4f}\")  \n",
    "\n",
    "# 打印分类报告  \n",
    "print(\"\\n分类报告：\")  \n",
    "print(classification_report(y_test, y_pred))  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**可能的原因**：\n",
    "- 数据预处理不当\n",
    "- 训练集和测试集划分问题\n",
    "- 类别极度不平衡\n",
    "- 特征选择不合理\n",
    "- 至少说明选择PCA预处理方案是不合适的"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 超参数优化\n",
    "**SVC算法的参数说明**\n",
    "\n",
    "- C (正则化参数)\n",
    "    - 控制模型的复杂度和对错误样本的容忍程度\n",
    "    - 较小的C值：\n",
    "        - 允许更多分类错误\n",
    "        - 模型复杂度低\n",
    "        - 正则化强度高\n",
    "        - 防止过拟合\n",
    "    - 较大的C值：\n",
    "        - 对错误样本的容忍度低\n",
    "        - 尽可能正确分类训练数据\n",
    "        - 模型复杂度高\n",
    "        - 可能导致过拟合\n",
    "- kernel (核函数类型)\n",
    "    - 将低维空间的非线性问题转化为高维空间的线性可分问题\n",
    "    - 避免直接计算高维空间的坐标转换\n",
    "    - 通过核技巧间接实现高维空间的映射\n",
    "    - 核函数例子：\n",
    "        - def kernel_function(x1, x2):  \n",
    "            - return (x1 * x2 + 1) ** 2  # 多项式核函数  \n",
    "    - 常见核函数：\n",
    "        - 'rbf'(径向基函数)：默认，处理非线性分类\n",
    "        - 'linear'：线性可分数据\n",
    "        - 'poly'：多项式核函数\n",
    "        - 'sigmoid'：神经网络风格的核函数\n",
    "- gamma (核函数系数)\n",
    "    - 定义单个训练样本对分类决策边界的影响范围\n",
    "    - 低gamma值：\n",
    "        - 样本影响范围大\n",
    "        - 决策边界较平滑\n",
    "        - 模型复杂度低\n",
    "    - 高gamma值：\n",
    "        - 样本影响范围小\n",
    "        - 决策边界更复杂\n",
    "        - 模型更容易捕捉局部特征\n",
    "        - 可能过拟合\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(  \n",
    "    X, y, test_size=0.2, random_state=42  \n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "最佳参数: {'C': 1, 'gamma': 'scale', 'kernel': 'rbf'}\n",
      "\n",
      "分类报告:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.71      0.79       282\n",
      "           1       0.53      0.78      0.63       118\n",
      "\n",
      "    accuracy                           0.73       400\n",
      "   macro avg       0.71      0.74      0.71       400\n",
      "weighted avg       0.78      0.73      0.74       400\n",
      "\n",
      "\n",
      "混淆矩阵:\n",
      " [[199  83]\n",
      " [ 26  92]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV \n",
    "from sklearn.metrics import classification_report, confusion_matrix  \n",
    "\n",
    "# 定义参数网格  \n",
    "param_grid = {  \n",
    "    'C': [0.1, 1, 10, 100],  \n",
    "    'gamma': ['scale', 'auto', 0.1, 1],  \n",
    "    'kernel': ['rbf', 'poly', 'sigmoid']  \n",
    "}  \n",
    "\n",
    "# 网格搜索  \n",
    "grid_search = GridSearchCV(  \n",
    "    SVC(class_weight='balanced'),   \n",
    "    param_grid,   \n",
    "    cv=5,  # 5折交叉验证，将数据集分成多个子集，反复训练和验证模型\n",
    "            #目的：评估模型的泛化性能，减少过拟合风险\n",
    "    scoring='f1',  \n",
    "    n_jobs=-1  # 使用所有可用的CPU核\n",
    ")  \n",
    "\n",
    "# 拟合模型  \n",
    "grid_search.fit(X_train, y_train)  \n",
    "\n",
    "# 最佳模型预测  \n",
    "best_model = grid_search.best_estimator_  # best_estimator_ 属性专用于存放最佳模型\n",
    "y_pred = best_model.predict(X_test)  \n",
    "\n",
    "# 输出结果  \n",
    "print(\"最佳参数:\", grid_search.best_params_)  \n",
    "print(\"\\n分类报告:\\n\", classification_report(y_test, y_pred))  \n",
    "print(\"\\n混淆矩阵:\\n\", confusion_matrix(y_test, y_pred))  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, y_pred)  \n",
    "plt.figure(figsize=(4,3))  \n",
    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues',   \n",
    "            xticklabels=['inactive', 'active'],  \n",
    "            yticklabels=['inactive', 'active'])  \n",
    "plt.title('Tubulin, SVM classfication')  \n",
    "plt.xlabel('pred')  \n",
    "plt.ylabel('real')  \n",
    "plt.tight_layout()  \n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 保存模型到本地硬盘\n",
    "- 推荐使用joblib，因为：\n",
    "     - 对numpy数组支持更好\n",
    "     - 序列化速度快\n",
    "     - 适合机器学习模型保存"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "import joblib  \n",
    "\n",
    "# 保存最优模型  \n",
    "joblib.dump(grid_search.best_estimator_, 'model/10_tubulin_SVC_model.pkl')  \n",
    "# 模型文件约8.5M（深度学习的模型在GB级别）\n",
    "\n",
    "# 加载模型  \n",
    "model = joblib.load('model/10_tubulin_SVC_model.pkl')  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(C=1, class_weight=&#x27;balanced&#x27;, probability=True)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SVC</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.svm.SVC.html\">?<span>Documentation for SVC</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>SVC(C=1, class_weight=&#x27;balanced&#x27;, probability=True)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "SVC(C=1, class_weight='balanced', probability=True)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 生产环境模拟\n",
    "- 搞清楚这个模型需要输入什么？什么形状的数组（矩阵）？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 264 entries, 0 to 263\n",
      "Data columns (total 17 columns):\n",
      " #   Column                    Non-Null Count  Dtype  \n",
      "---  ------                    --------------  -----  \n",
      " 0   Average Rt(min)           264 non-null    float64\n",
      " 1   Average Mz                264 non-null    float64\n",
      " 2   Metabolite name           264 non-null    object \n",
      " 3   Adduct type               264 non-null    object \n",
      " 4   Formula                   264 non-null    object \n",
      " 5   Ontology                  48 non-null     object \n",
      " 6   INCHIKEY                  264 non-null    object \n",
      " 7   SMILES                    264 non-null    object \n",
      " 8   MS/MS matched             264 non-null    bool   \n",
      " 9   Matched peaks count       264 non-null    int64  \n",
      " 10  Matched peaks percentage  264 non-null    float64\n",
      " 11  Total score               264 non-null    float64\n",
      " 12  G1_2                      264 non-null    float64\n",
      " 13  G1_3                      264 non-null    float64\n",
      " 14  G2_1                      264 non-null    float64\n",
      " 15  G2_2                      264 non-null    float64\n",
      " 16  G2_3                      264 non-null    float64\n",
      "dtypes: bool(1), float64(9), int64(1), object(6)\n",
      "memory usage: 33.4+ KB\n"
     ]
    }
   ],
   "source": [
    "test = pd.read_csv('data/LC-MS.csv')\n",
    "test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(264, 1024)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mols_test = test['SMILES'].apply(Chem.MolFromSmiles)\n",
    "fps_test = mols_test.apply(fp_generator.GetFingerprint)\n",
    "\n",
    "X1 = np.array([np.array(fp) for fp in fps_test]) \n",
    "X1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(264,)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y1 = model.predict(X1)\n",
    "y1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1]), array([245,  19]))"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(y1, return_counts=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 38,  40,  42,  43,  48,  49,  56,  57,  76,  79, 101, 127, 173,\n",
       "        174, 195, 236, 240, 241, 256]),)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.where(y1 == 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Average Rt(min)</th>\n",
       "      <th>Average Mz</th>\n",
       "      <th>Metabolite name</th>\n",
       "      <th>Adduct type</th>\n",
       "      <th>Formula</th>\n",
       "      <th>Ontology</th>\n",
       "      <th>INCHIKEY</th>\n",
       "      <th>SMILES</th>\n",
       "      <th>MS/MS matched</th>\n",
       "      <th>Matched peaks count</th>\n",
       "      <th>Matched peaks percentage</th>\n",
       "      <th>Total score</th>\n",
       "      <th>G1_2</th>\n",
       "      <th>G1_3</th>\n",
       "      <th>G2_1</th>\n",
       "      <th>G2_2</th>\n",
       "      <th>G2_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>7.563</td>\n",
       "      <td>122.09624</td>\n",
       "      <td>C11004;2,6-Xylidine;MoNA</td>\n",
       "      <td>[M+H]+</td>\n",
       "      <td>C8H11N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>UFFBMTHBGFGIHF-UHFFFAOYSA-N</td>\n",
       "      <td>CC1=CC=CC(C)=C1N</td>\n",
       "      <td>True</td>\n",
       "      <td>3</td>\n",
       "      <td>0.67</td>\n",
       "      <td>1.51</td>\n",
       "      <td>9.516031e+07</td>\n",
       "      <td>1.741844e+08</td>\n",
       "      <td>1.526194e+08</td>\n",
       "      <td>1.320400e+08</td>\n",
       "      <td>1.170786e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>3.380</td>\n",
       "      <td>123.05511</td>\n",
       "      <td>C00153;Niacinamide;msd</td>\n",
       "      <td>[M+H]+</td>\n",
       "      <td>C6H6N2O</td>\n",
       "      <td>Nicotinamides</td>\n",
       "      <td>DFPAKSUCGFBDDF-UHFFFAOYSA-N</td>\n",
       "      <td>NC(=O)C1=CC=CN=C1</td>\n",
       "      <td>True</td>\n",
       "      <td>7</td>\n",
       "      <td>0.57</td>\n",
       "      <td>1.69</td>\n",
       "      <td>4.641280e+08</td>\n",
       "      <td>6.312765e+08</td>\n",
       "      <td>4.816682e+08</td>\n",
       "      <td>6.949458e+08</td>\n",
       "      <td>5.642415e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>1.253</td>\n",
       "      <td>130.04970</td>\n",
       "      <td>C01879;PYROGLUTAMIC ACID;MoNA</td>\n",
       "      <td>[M+H]+</td>\n",
       "      <td>C5H7NO3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ODHCTXKNWHHXJC-VKHMYHEASA-N</td>\n",
       "      <td>O=C(O)C1N=C(O)CC1</td>\n",
       "      <td>True</td>\n",
       "      <td>4</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.74</td>\n",
       "      <td>4.746414e+07</td>\n",
       "      <td>8.311464e+07</td>\n",
       "      <td>6.065862e+07</td>\n",
       "      <td>6.384861e+07</td>\n",
       "      <td>5.660783e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>5.907</td>\n",
       "      <td>130.06483</td>\n",
       "      <td>C06413;Quinoline; LC-tDDA; CE40;MoNA</td>\n",
       "      <td>[M+H]+</td>\n",
       "      <td>C9H7N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SMWDFEZZVXVKRB-UHFFFAOYSA-N</td>\n",
       "      <td>N=1C=CC=C2C=CC=CC12</td>\n",
       "      <td>True</td>\n",
       "      <td>8</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.34</td>\n",
       "      <td>3.422896e+07</td>\n",
       "      <td>3.406377e+07</td>\n",
       "      <td>3.657957e+07</td>\n",
       "      <td>3.572551e+07</td>\n",
       "      <td>3.399348e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>1.625</td>\n",
       "      <td>133.03160</td>\n",
       "      <td>C11074;C11074;CFMID</td>\n",
       "      <td>[M+H]+</td>\n",
       "      <td>C5H8O2S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MZOYMQRKTJRHGJ-UHFFFAOYSA-N</td>\n",
       "      <td>O=C(O)C1CCCS1</td>\n",
       "      <td>True</td>\n",
       "      <td>8</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.47</td>\n",
       "      <td>6.875503e+07</td>\n",
       "      <td>8.260777e+07</td>\n",
       "      <td>8.724677e+07</td>\n",
       "      <td>8.355135e+07</td>\n",
       "      <td>8.676998e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>1.955</td>\n",
       "      <td>133.03162</td>\n",
       "      <td>C11074;C11074;CFMID</td>\n",
       "      <td>[M+H]+</td>\n",
       "      <td>C5H8O2S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MZOYMQRKTJRHGJ-UHFFFAOYSA-N</td>\n",
       "      <td>O=C(O)C1CCCS1</td>\n",
       "      <td>True</td>\n",
       "      <td>8</td>\n",
       "      <td>0.63</td>\n",
       "      <td>1.58</td>\n",
       "      <td>1.420473e+08</td>\n",
       "      <td>2.211838e+08</td>\n",
       "      <td>1.014598e+08</td>\n",
       "      <td>2.181175e+08</td>\n",
       "      <td>2.020923e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>10.454</td>\n",
       "      <td>139.01770</td>\n",
       "      <td>C00230;3,4-DIHYDROXYBENZOIC ACID;inhouse</td>\n",
       "      <td>[M-UNK]+</td>\n",
       "      <td>C7H6O4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>YQUVCSBJEUQKSH-UHFFFAOYSA-N</td>\n",
       "      <td>OC(C1=CC(O)=C(O)C=C1)=O</td>\n",
       "      <td>True</td>\n",
       "      <td>4</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.58</td>\n",
       "      <td>1.536586e+07</td>\n",
       "      <td>1.261390e+07</td>\n",
       "      <td>1.626884e+07</td>\n",
       "      <td>2.599605e+07</td>\n",
       "      <td>2.240111e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>2.578</td>\n",
       "      <td>139.05009</td>\n",
       "      <td>C00785;Urocanic acid;inhouse</td>\n",
       "      <td>[M+H]+</td>\n",
       "      <td>C6H6N2O2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LOIYMIARKYCTBW-OWOJBTEDSA-N</td>\n",
       "      <td>O=C(O)/C=C/C1=CN=CN1</td>\n",
       "      <td>True</td>\n",
       "      <td>18</td>\n",
       "      <td>0.39</td>\n",
       "      <td>1.57</td>\n",
       "      <td>3.254219e+07</td>\n",
       "      <td>3.456938e+07</td>\n",
       "      <td>3.674522e+07</td>\n",
       "      <td>2.647834e+07</td>\n",
       "      <td>2.795591e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>4.541</td>\n",
       "      <td>149.05957</td>\n",
       "      <td>C00423;Trans-Cinnamic acid;msd</td>\n",
       "      <td>[M+H]+</td>\n",
       "      <td>C9H8O2</td>\n",
       "      <td>Cinnamic acids</td>\n",
       "      <td>WBYWAXJHAXSJNI-VOTSOKGWSA-N</td>\n",
       "      <td>O=C(O)\\C=C\\c1ccccc1</td>\n",
       "      <td>True</td>\n",
       "      <td>6</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.51</td>\n",
       "      <td>1.080314e+08</td>\n",
       "      <td>1.052624e+08</td>\n",
       "      <td>1.362832e+08</td>\n",
       "      <td>1.381845e+08</td>\n",
       "      <td>1.231428e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>4.472</td>\n",
       "      <td>152.05659</td>\n",
       "      <td>C00242;Guanine;msd</td>\n",
       "      <td>[M+H]+</td>\n",
       "      <td>C5H5N5O</td>\n",
       "      <td>Purines and purine derivatives</td>\n",
       "      <td>UYTPUPDQBNUYGX-UHFFFAOYSA-N</td>\n",
       "      <td>c1[nH]c2c(=O)[nH]c(nc2n1)N</td>\n",
       "      <td>True</td>\n",
       "      <td>6</td>\n",
       "      <td>0.67</td>\n",
       "      <td>1.74</td>\n",
       "      <td>3.658251e+07</td>\n",
       "      <td>3.334337e+07</td>\n",
       "      <td>4.031269e+07</td>\n",
       "      <td>4.490048e+07</td>\n",
       "      <td>4.658388e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>1.401</td>\n",
       "      <td>170.03246</td>\n",
       "      <td>C02376;5-Methylcytosine;inhouse</td>\n",
       "      <td>[M+UNK]+</td>\n",
       "      <td>C5H7N3O</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LRSASMSXMSNRBT-UHFFFAOYSA-N</td>\n",
       "      <td>O=C1N=CC(C)=C(N)N1</td>\n",
       "      <td>True</td>\n",
       "      <td>3</td>\n",
       "      <td>0.33</td>\n",
       "      <td>1.36</td>\n",
       "      <td>1.092526e+09</td>\n",
       "      <td>1.313862e+09</td>\n",
       "      <td>1.185177e+09</td>\n",
       "      <td>1.146511e+09</td>\n",
       "      <td>1.007472e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>13.971</td>\n",
       "      <td>183.08020</td>\n",
       "      <td>C06354;Benzophenone;msd</td>\n",
       "      <td>[M+H]+</td>\n",
       "      <td>C13H10O</td>\n",
       "      <td>Benzophenones</td>\n",
       "      <td>RWCCWEUUXYIKHB-UHFFFAOYSA-N</td>\n",
       "      <td>O=C(c1ccccc1)c2ccccc2</td>\n",
       "      <td>True</td>\n",
       "      <td>3</td>\n",
       "      <td>0.33</td>\n",
       "      <td>1.59</td>\n",
       "      <td>7.051981e+07</td>\n",
       "      <td>7.778214e+07</td>\n",
       "      <td>8.568951e+07</td>\n",
       "      <td>8.584375e+07</td>\n",
       "      <td>8.804868e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>11.809</td>\n",
       "      <td>223.09627</td>\n",
       "      <td>C14175;Diethyl-phthalate;msd</td>\n",
       "      <td>[M+H]+</td>\n",
       "      <td>C12H14O4</td>\n",
       "      <td>Benzoic acid esters</td>\n",
       "      <td>FLKPEMZONWLCSK-UHFFFAOYSA-N</td>\n",
       "      <td>CCOC(=O)C1=CC=CC=C1C(=O)OCC</td>\n",
       "      <td>True</td>\n",
       "      <td>6</td>\n",
       "      <td>0.67</td>\n",
       "      <td>1.73</td>\n",
       "      <td>7.609256e+06</td>\n",
       "      <td>1.734946e+07</td>\n",
       "      <td>5.815676e+06</td>\n",
       "      <td>5.713137e+06</td>\n",
       "      <td>4.945714e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>11.907</td>\n",
       "      <td>223.09630</td>\n",
       "      <td>C14175;Diethyl-phthalate;msd</td>\n",
       "      <td>[M+H]+</td>\n",
       "      <td>C12H14O4</td>\n",
       "      <td>Benzoic acid esters</td>\n",
       "      <td>FLKPEMZONWLCSK-UHFFFAOYSA-N</td>\n",
       "      <td>CCOC(=O)C1=CC=CC=C1C(=O)OCC</td>\n",
       "      <td>True</td>\n",
       "      <td>6</td>\n",
       "      <td>0.83</td>\n",
       "      <td>1.75</td>\n",
       "      <td>9.071398e+06</td>\n",
       "      <td>1.637007e+07</td>\n",
       "      <td>8.018424e+06</td>\n",
       "      <td>7.010724e+06</td>\n",
       "      <td>5.701542e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>6.612</td>\n",
       "      <td>298.09659</td>\n",
       "      <td>C00170;5-S-Methylthioadenosine; LC-tDDA; CE30;...</td>\n",
       "      <td>[M+H]+</td>\n",
       "      <td>C11H15N5O3S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WUUGFSXJNOTRMR-IOSLPCCCSA-N</td>\n",
       "      <td>OC1C(O)C(OC1N2C=NC=3C(=NC=NC32)N)CSC</td>\n",
       "      <td>True</td>\n",
       "      <td>6</td>\n",
       "      <td>0.67</td>\n",
       "      <td>1.81</td>\n",
       "      <td>3.232882e+08</td>\n",
       "      <td>3.711102e+08</td>\n",
       "      <td>3.532815e+08</td>\n",
       "      <td>3.347730e+08</td>\n",
       "      <td>3.225259e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>18.345</td>\n",
       "      <td>408.36795</td>\n",
       "      <td>C10354;2-(Hydroxymethyl)anthraquinone;inhouse</td>\n",
       "      <td>[M+UNK]+</td>\n",
       "      <td>C15H10O3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>JYKHAJGLEVKEAA-UHFFFAOYSA-N</td>\n",
       "      <td>O=C1C2=C(C=C(CO)C=C2)C(C3=C1C=CC=C3)=O</td>\n",
       "      <td>True</td>\n",
       "      <td>21</td>\n",
       "      <td>0.33</td>\n",
       "      <td>1.70</td>\n",
       "      <td>1.549098e+07</td>\n",
       "      <td>2.959743e+07</td>\n",
       "      <td>5.768400e+06</td>\n",
       "      <td>2.313568e+07</td>\n",
       "      <td>1.431721e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>18.756</td>\n",
       "      <td>419.31482</td>\n",
       "      <td>C00230;3,4-DIHYDROXYBENZOIC ACID;inhouse</td>\n",
       "      <td>[M+UNK]+</td>\n",
       "      <td>C7H6O4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>YQUVCSBJEUQKSH-UHFFFAOYSA-N</td>\n",
       "      <td>OC1=CC(C(O)=O)=CC=C1O</td>\n",
       "      <td>True</td>\n",
       "      <td>20</td>\n",
       "      <td>0.30</td>\n",
       "      <td>1.68</td>\n",
       "      <td>1.005068e+07</td>\n",
       "      <td>8.540446e+06</td>\n",
       "      <td>7.523510e+06</td>\n",
       "      <td>4.023483e+07</td>\n",
       "      <td>1.415298e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>18.889</td>\n",
       "      <td>419.31512</td>\n",
       "      <td>C00230;3,4-DIHYDROXYBENZOIC ACID;inhouse</td>\n",
       "      <td>[M+UNK]+</td>\n",
       "      <td>C7H6O4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>YQUVCSBJEUQKSH-UHFFFAOYSA-N</td>\n",
       "      <td>OC1=CC(C(O)=O)=CC=C1O</td>\n",
       "      <td>True</td>\n",
       "      <td>20</td>\n",
       "      <td>0.25</td>\n",
       "      <td>1.61</td>\n",
       "      <td>9.494429e+06</td>\n",
       "      <td>1.377020e+07</td>\n",
       "      <td>8.728594e+06</td>\n",
       "      <td>3.038734e+07</td>\n",
       "      <td>7.954864e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>17.923</td>\n",
       "      <td>628.19391</td>\n",
       "      <td>C01424;Gallic acid;inhouse</td>\n",
       "      <td>[M+UNK]+</td>\n",
       "      <td>C7H6O5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LNTHITQWFMADLM-UHFFFAOYSA-N</td>\n",
       "      <td>OC1=CC(C(O)=O)=CC(O)=C1O</td>\n",
       "      <td>True</td>\n",
       "      <td>42</td>\n",
       "      <td>0.29</td>\n",
       "      <td>1.46</td>\n",
       "      <td>3.445800e+07</td>\n",
       "      <td>3.194048e+07</td>\n",
       "      <td>9.259415e+06</td>\n",
       "      <td>2.984917e+07</td>\n",
       "      <td>2.825207e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Average Rt(min)  Average Mz  \\\n",
       "38             7.563   122.09624   \n",
       "40             3.380   123.05511   \n",
       "42             1.253   130.04970   \n",
       "43             5.907   130.06483   \n",
       "48             1.625   133.03160   \n",
       "49             1.955   133.03162   \n",
       "56            10.454   139.01770   \n",
       "57             2.578   139.05009   \n",
       "76             4.541   149.05957   \n",
       "79             4.472   152.05659   \n",
       "101            1.401   170.03246   \n",
       "127           13.971   183.08020   \n",
       "173           11.809   223.09627   \n",
       "174           11.907   223.09630   \n",
       "195            6.612   298.09659   \n",
       "236           18.345   408.36795   \n",
       "240           18.756   419.31482   \n",
       "241           18.889   419.31512   \n",
       "256           17.923   628.19391   \n",
       "\n",
       "                                       Metabolite name Adduct type  \\\n",
       "38                            C11004;2,6-Xylidine;MoNA      [M+H]+   \n",
       "40                              C00153;Niacinamide;msd      [M+H]+   \n",
       "42                       C01879;PYROGLUTAMIC ACID;MoNA      [M+H]+   \n",
       "43                C06413;Quinoline; LC-tDDA; CE40;MoNA      [M+H]+   \n",
       "48                                 C11074;C11074;CFMID      [M+H]+   \n",
       "49                                 C11074;C11074;CFMID      [M+H]+   \n",
       "56            C00230;3,4-DIHYDROXYBENZOIC ACID;inhouse    [M-UNK]+   \n",
       "57                        C00785;Urocanic acid;inhouse      [M+H]+   \n",
       "76                      C00423;Trans-Cinnamic acid;msd      [M+H]+   \n",
       "79                                  C00242;Guanine;msd      [M+H]+   \n",
       "101                    C02376;5-Methylcytosine;inhouse    [M+UNK]+   \n",
       "127                            C06354;Benzophenone;msd      [M+H]+   \n",
       "173                       C14175;Diethyl-phthalate;msd      [M+H]+   \n",
       "174                       C14175;Diethyl-phthalate;msd      [M+H]+   \n",
       "195  C00170;5-S-Methylthioadenosine; LC-tDDA; CE30;...      [M+H]+   \n",
       "236      C10354;2-(Hydroxymethyl)anthraquinone;inhouse    [M+UNK]+   \n",
       "240           C00230;3,4-DIHYDROXYBENZOIC ACID;inhouse    [M+UNK]+   \n",
       "241           C00230;3,4-DIHYDROXYBENZOIC ACID;inhouse    [M+UNK]+   \n",
       "256                         C01424;Gallic acid;inhouse    [M+UNK]+   \n",
       "\n",
       "         Formula                        Ontology                     INCHIKEY  \\\n",
       "38        C8H11N                             NaN  UFFBMTHBGFGIHF-UHFFFAOYSA-N   \n",
       "40       C6H6N2O                   Nicotinamides  DFPAKSUCGFBDDF-UHFFFAOYSA-N   \n",
       "42       C5H7NO3                             NaN  ODHCTXKNWHHXJC-VKHMYHEASA-N   \n",
       "43         C9H7N                             NaN  SMWDFEZZVXVKRB-UHFFFAOYSA-N   \n",
       "48       C5H8O2S                             NaN  MZOYMQRKTJRHGJ-UHFFFAOYSA-N   \n",
       "49       C5H8O2S                             NaN  MZOYMQRKTJRHGJ-UHFFFAOYSA-N   \n",
       "56        C7H6O4                             NaN  YQUVCSBJEUQKSH-UHFFFAOYSA-N   \n",
       "57      C6H6N2O2                             NaN  LOIYMIARKYCTBW-OWOJBTEDSA-N   \n",
       "76        C9H8O2                  Cinnamic acids  WBYWAXJHAXSJNI-VOTSOKGWSA-N   \n",
       "79       C5H5N5O  Purines and purine derivatives  UYTPUPDQBNUYGX-UHFFFAOYSA-N   \n",
       "101      C5H7N3O                             NaN  LRSASMSXMSNRBT-UHFFFAOYSA-N   \n",
       "127      C13H10O                   Benzophenones  RWCCWEUUXYIKHB-UHFFFAOYSA-N   \n",
       "173     C12H14O4             Benzoic acid esters  FLKPEMZONWLCSK-UHFFFAOYSA-N   \n",
       "174     C12H14O4             Benzoic acid esters  FLKPEMZONWLCSK-UHFFFAOYSA-N   \n",
       "195  C11H15N5O3S                             NaN  WUUGFSXJNOTRMR-IOSLPCCCSA-N   \n",
       "236     C15H10O3                             NaN  JYKHAJGLEVKEAA-UHFFFAOYSA-N   \n",
       "240       C7H6O4                             NaN  YQUVCSBJEUQKSH-UHFFFAOYSA-N   \n",
       "241       C7H6O4                             NaN  YQUVCSBJEUQKSH-UHFFFAOYSA-N   \n",
       "256       C7H6O5                             NaN  LNTHITQWFMADLM-UHFFFAOYSA-N   \n",
       "\n",
       "                                     SMILES  MS/MS matched  \\\n",
       "38                         CC1=CC=CC(C)=C1N           True   \n",
       "40                        NC(=O)C1=CC=CN=C1           True   \n",
       "42                        O=C(O)C1N=C(O)CC1           True   \n",
       "43                      N=1C=CC=C2C=CC=CC12           True   \n",
       "48                            O=C(O)C1CCCS1           True   \n",
       "49                            O=C(O)C1CCCS1           True   \n",
       "56                  OC(C1=CC(O)=C(O)C=C1)=O           True   \n",
       "57                     O=C(O)/C=C/C1=CN=CN1           True   \n",
       "76                      O=C(O)\\C=C\\c1ccccc1           True   \n",
       "79               c1[nH]c2c(=O)[nH]c(nc2n1)N           True   \n",
       "101                      O=C1N=CC(C)=C(N)N1           True   \n",
       "127                   O=C(c1ccccc1)c2ccccc2           True   \n",
       "173             CCOC(=O)C1=CC=CC=C1C(=O)OCC           True   \n",
       "174             CCOC(=O)C1=CC=CC=C1C(=O)OCC           True   \n",
       "195    OC1C(O)C(OC1N2C=NC=3C(=NC=NC32)N)CSC           True   \n",
       "236  O=C1C2=C(C=C(CO)C=C2)C(C3=C1C=CC=C3)=O           True   \n",
       "240                   OC1=CC(C(O)=O)=CC=C1O           True   \n",
       "241                   OC1=CC(C(O)=O)=CC=C1O           True   \n",
       "256                OC1=CC(C(O)=O)=CC(O)=C1O           True   \n",
       "\n",
       "     Matched peaks count  Matched peaks percentage  Total score          G1_2  \\\n",
       "38                     3                      0.67         1.51  9.516031e+07   \n",
       "40                     7                      0.57         1.69  4.641280e+08   \n",
       "42                     4                      0.50         1.74  4.746414e+07   \n",
       "43                     8                      0.50         1.34  3.422896e+07   \n",
       "48                     8                      0.50         1.47  6.875503e+07   \n",
       "49                     8                      0.63         1.58  1.420473e+08   \n",
       "56                     4                      0.50         1.58  1.536586e+07   \n",
       "57                    18                      0.39         1.57  3.254219e+07   \n",
       "76                     6                      0.50         1.51  1.080314e+08   \n",
       "79                     6                      0.67         1.74  3.658251e+07   \n",
       "101                    3                      0.33         1.36  1.092526e+09   \n",
       "127                    3                      0.33         1.59  7.051981e+07   \n",
       "173                    6                      0.67         1.73  7.609256e+06   \n",
       "174                    6                      0.83         1.75  9.071398e+06   \n",
       "195                    6                      0.67         1.81  3.232882e+08   \n",
       "236                   21                      0.33         1.70  1.549098e+07   \n",
       "240                   20                      0.30         1.68  1.005068e+07   \n",
       "241                   20                      0.25         1.61  9.494429e+06   \n",
       "256                   42                      0.29         1.46  3.445800e+07   \n",
       "\n",
       "             G1_3          G2_1          G2_2          G2_3  \n",
       "38   1.741844e+08  1.526194e+08  1.320400e+08  1.170786e+08  \n",
       "40   6.312765e+08  4.816682e+08  6.949458e+08  5.642415e+08  \n",
       "42   8.311464e+07  6.065862e+07  6.384861e+07  5.660783e+07  \n",
       "43   3.406377e+07  3.657957e+07  3.572551e+07  3.399348e+07  \n",
       "48   8.260777e+07  8.724677e+07  8.355135e+07  8.676998e+07  \n",
       "49   2.211838e+08  1.014598e+08  2.181175e+08  2.020923e+08  \n",
       "56   1.261390e+07  1.626884e+07  2.599605e+07  2.240111e+07  \n",
       "57   3.456938e+07  3.674522e+07  2.647834e+07  2.795591e+07  \n",
       "76   1.052624e+08  1.362832e+08  1.381845e+08  1.231428e+08  \n",
       "79   3.334337e+07  4.031269e+07  4.490048e+07  4.658388e+07  \n",
       "101  1.313862e+09  1.185177e+09  1.146511e+09  1.007472e+09  \n",
       "127  7.778214e+07  8.568951e+07  8.584375e+07  8.804868e+07  \n",
       "173  1.734946e+07  5.815676e+06  5.713137e+06  4.945714e+06  \n",
       "174  1.637007e+07  8.018424e+06  7.010724e+06  5.701542e+06  \n",
       "195  3.711102e+08  3.532815e+08  3.347730e+08  3.225259e+08  \n",
       "236  2.959743e+07  5.768400e+06  2.313568e+07  1.431721e+07  \n",
       "240  8.540446e+06  7.523510e+06  4.023483e+07  1.415298e+07  \n",
       "241  1.377020e+07  8.728594e+06  3.038734e+07  7.954864e+06  \n",
       "256  3.194048e+07  9.259415e+06  2.984917e+07  2.825207e+07  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 显示阳性分子的信息\n",
    "test.iloc[np.where(y1 == 1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 38,  40,  42,  43,  48,  49,  56,  57,  76,  79, 101, 127, 173,\n",
       "        174, 195, 236, 240, 241, 256]),)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.where(y1 == 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(mols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
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       "<path class=\"bond-20 atom-8 atom-21\" d=\"M 250.2,4754.7 L 248.7,4765.1\" style=\"fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
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       "<path class=\"bond-23 atom-23 atom-24\" d=\"M 168.2,4763.6 L 147.3,4760.5\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
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       "<path class=\"atom-1\" d=\"M 106.5 4736.6 Q 106.5 4734.9, 107.4 4733.9 Q 108.2 4732.9, 109.8 4732.9 Q 111.4 4732.9, 112.3 4733.9 Q 113.2 4734.9, 113.2 4736.6 Q 113.2 4738.4, 112.3 4739.4 Q 111.4 4740.4, 109.8 4740.4 Q 108.2 4740.4, 107.4 4739.4 Q 106.5 4738.4, 106.5 4736.6 M 109.8 4739.5 Q 110.9 4739.5, 111.5 4738.8 Q 112.1 4738.1, 112.1 4736.6 Q 112.1 4735.2, 111.5 4734.5 Q 110.9 4733.7, 109.8 4733.7 Q 108.7 4733.7, 108.1 4734.5 Q 107.5 4735.2, 107.5 4736.6 Q 107.5 4738.1, 108.1 4738.8 Q 108.7 4739.5, 109.8 4739.5 \" fill=\"#FF0000\"/>\n",
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       "<path class=\"atom-13\" d=\"M 320.7 4774.2 L 323.1 4778.1 Q 323.3 4778.5, 323.7 4779.1 Q 324.1 4779.8, 324.1 4779.9 L 324.1 4774.2 L 325.0 4774.2 L 325.0 4781.5 L 324.1 4781.5 L 321.5 4777.3 Q 321.2 4776.8, 320.9 4776.2 Q 320.6 4775.7, 320.5 4775.5 L 320.5 4781.5 L 319.6 4781.5 L 319.6 4774.2 L 320.7 4774.2 \" fill=\"#0000FF\"/>\n",
       "<path class=\"atom-13\" d=\"M 326.5 4774.2 L 327.4 4774.2 L 327.4 4777.3 L 331.1 4777.3 L 331.1 4774.2 L 332.1 4774.2 L 332.1 4781.5 L 331.1 4781.5 L 331.1 4778.1 L 327.4 4778.1 L 327.4 4781.5 L 326.5 4781.5 L 326.5 4774.2 \" fill=\"#0000FF\"/>\n",
       "<path class=\"atom-17\" d=\"M 392.4 4723.9 Q 392.4 4722.1, 393.2 4721.2 Q 394.0 4720.2, 395.7 4720.2 Q 397.1 4720.2, 397.9 4721.3 L 397.3 4721.8 Q 396.7 4721.1, 395.7 4721.1 Q 394.6 4721.1, 394.0 4721.8 Q 393.4 4722.5, 393.4 4723.9 Q 393.4 4725.4, 394.0 4726.1 Q 394.6 4726.8, 395.8 4726.8 Q 396.6 4726.8, 397.5 4726.3 L 397.8 4727.1 Q 397.4 4727.4, 396.8 4727.5 Q 396.3 4727.6, 395.6 4727.6 Q 394.0 4727.6, 393.2 4726.7 Q 392.4 4725.7, 392.4 4723.9 \" fill=\"#00CC00\"/>\n",
       "<path class=\"atom-17\" d=\"M 398.7 4719.8 L 399.6 4719.8 L 399.6 4727.6 L 398.7 4727.6 L 398.7 4719.8 \" fill=\"#00CC00\"/>\n",
       "<path class=\"atom-21\" d=\"M 246.4 4766.0 L 248.8 4769.8 Q 249.0 4770.2, 249.4 4770.9 Q 249.8 4771.6, 249.8 4771.6 L 249.8 4766.0 L 250.8 4766.0 L 250.8 4773.2 L 249.8 4773.2 L 247.2 4769.0 Q 246.9 4768.5, 246.6 4768.0 Q 246.3 4767.4, 246.2 4767.2 L 246.2 4773.2 L 245.3 4773.2 L 245.3 4766.0 L 246.4 4766.0 \" fill=\"#0000FF\"/>\n",
       "<path class=\"atom-21\" d=\"M 252.2 4766.0 L 253.1 4766.0 L 253.1 4769.1 L 256.8 4769.1 L 256.8 4766.0 L 257.8 4766.0 L 257.8 4773.2 L 256.8 4773.2 L 256.8 4769.9 L 253.1 4769.9 L 253.1 4773.2 L 252.2 4773.2 L 252.2 4766.0 \" fill=\"#0000FF\"/>\n",
       "<path class=\"atom-22\" d=\"M 221.2 4770.3 L 223.6 4774.1 Q 223.8 4774.5, 224.2 4775.2 Q 224.6 4775.9, 224.6 4775.9 L 224.6 4770.3 L 225.6 4770.3 L 225.6 4777.5 L 224.6 4777.5 L 222.0 4773.3 Q 221.7 4772.8, 221.4 4772.3 Q 221.1 4771.7, 221.0 4771.5 L 221.0 4777.5 L 220.1 4777.5 L 220.1 4770.3 L 221.2 4770.3 \" fill=\"#0000FF\"/>\n",
       "</svg>"
      ],
      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from rdkit.Chem import Draw\n",
    "\n",
    "Draw.MolsToGridImage(  \n",
    "        mols.iloc[np.where(y1 == 1)].tolist(),   \n",
    "        molsPerRow=2,  \n",
    "        subImgSize=(500,500),  \n",
    "        # legends=legends,  \n",
    "        useSVG=True \n",
    "    )  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
